Prepared by Fayes Salma

 Introduction: Financial Tasks  Data Mining process  Methods in Financial Data mining o Neural Network o Decision Tree  Trading Strategies  Conclusion

Financial data mining is a data mining used in financial market. It uses the same techniques to generate results which are used in the financial tasks.

 Forecasting stock market The stock market is one of the most important sources for companies to raise money. This allows businesses to be publicly traded, or raise additional capital for expansion by selling shares of ownership of the company in a public market  Currency exchange rate The foreign exchange market is where banks and other official institutions facilitate the buying and selling of foreign currencies, one of the largest and most liquid financial markets in the world  Bank bankruptcy

 Credit rating Credit ratings are calculated from financial history and current assets and liabilities The factors which may influence a person's credit rating are:  ability to pay a loan  saving patterns  spending patterns  debt

 Loan management  money laundering analyses is the practice of disguising illegally obtained funds so that they seem legal.  Understanding and managing financial risk probability of actual return being less than expected return

 Data mining as a process of discovering useful patterns.

To:  forecast multidimensional time series  accommodate specific efficiency criteria  be able to explain the forecast and the forecasting model  be able to benefit from very subtle patterns with a short life time

 Expected Goals: ◦ Make decision about the stocks (buy, sell, keep) ◦ Make decisions about loans (Give a loan, loan value, loan duration, …) ◦ Detecting the frauds and money laundering

 Time series analysis to predict the future trends on the base of past price patterns X t

 Data selection and forecast horizon  Data Selection: is tightly connected to the selection of the target variable  Forecast horizon long-horizon returns could be forecast better than short-horizon returns depending on the training data used and model parameters

 Measures of success deviation between forecast and actual values on training and testing data

 Attribute-based methodologies each object x is given by a set of values A1(x), A2(x),…,An(x) It covers a wide range of statistical and connectionist (neural network) methods.  relational methodologies objects are represented by their relations with other objects, for instance, x>y, y z.

 very data mining method and technique has been used in financial modeling  linear and non-linear models  multi-layer neural networks  hierarchical clustering  k-nearest neighbors  decision tree analysis  regression  Bayesian learning

 Steps in designing a neural network forecasting model  Variable selection  Data collection  Data preprocessing  Training, testing and evaluation sets  Neural Network paradigms  Number of hidden layers  Number of hidden neurons  Number of output neurons  Transfer function  Neural network training  number of training iteration  learning rate  Implementation

 Variable selection Knowing which input variable are important in the market being forecasted is critical using economy theory in choosing variable which are likely important predictors.

Data collection Must consider cost and availability when collecting data for the variables chosen in the first step

 Data preprocessing Analyzing and transforming the input and output variables to minimize noise, highlight important relationships, flatten the distribution of the variable to assist the neural network in learning the relevant pattern

 Training set is the largest set and isused by the neural network to learn the patterns present in the data  The testing ranging in size from 10% to 30 % of the training set

 Neural Network paradigms  Number of hidden layers It is recommended that all nueral networks should stat with preferbly one or at most two hidden layers

 Neural Network paradigms  Number of hidden neurons Consider n input neurons and m output neurons, the hidden neurons would have sqrt(n*m) neurons

 Neural Network paradigms  Number of output neurons Depending on the problem

Transfer function Linear transfer functions are not useful for nonlinear mapping and classification. Financial markets are nonlinear, such as sigmoid function

 Neural network training  Number of training iteration  First view The training shouldn’t stop until there is no improvement in the error function  Second view Training is stopped after predetermined number of iterations

 time delay networks  Elman networks  Jordan networks  GMDH  milti-recurrent networks

 DT are often used in credit scoring problems in order to describe and classify good or bad clients of a bank on the basis of socioeconomic indicators (e.g., age, working conditions, family status, etc.) and financial conditions (e.g., income, savings, payment methods, etc.

 Before starting the experiment, we need to specify the knowledge we want to extract, because the knowledge specificity determines what kind of mining algorithm to be chosen.

 After the neural network forecasting model is developed, the network is trained to forecast future trend. However, our interest is not only to construct a forecasting model, but also to make money

 COPLINK project to detecting Crime Data Mining

Questions?